[ROCm] Reduce AITER MLA FP8 BMM warmup sizes

[rohash123]

Purpose

Reduce ROCm AITER MLA FP8 BMM startup work by precompiling only the real decode sizes that are covered by the configured CUDA graph limit.

The previous path precompiled every FP8 BMM size from 1..1024 for every MLA layer. This patch keeps a contiguous warmup range, because MLAAttention.forward_impl strips CUDA-graph padding before this BMM path, but caps that range at the graph-covered decode size when CUDA graphs are enabled. Larger batches can still JIT later, but they are already outside the captured fast path.

The AITER Triton FP8 BMM kernel specializes on the query-row count. Warming sizes that vLLM will not use for captured decode increases model loading time before the server can handle requests.

For the tested DeepSeek-V2-Lite ROCm config, max_cudagraph_capture_size is 512, so this changes each MLA layer's FP8 BMM warmup from 1..1024 to 1..512. Small odd actual decode sizes, such as batch 7, remain covered because the warmup range is still contiguous.

This PR was developed with AI assistance. I reviewed the changed code paths, ran the checks below, and verified the ROCm smoke tests on MI300X.

Duplicate-work check

No linked issue exists for this change. I checked for open duplicate work with:

• gh pr list --repo vllm-project/vllm --state open --search "AITER MLA FP8 BMM warmup"
• gh pr list --repo vllm-project/vllm --state open --search "rocm aiter mla fp8 bmm"
• gh pr list --repo vllm-project/vllm --state open --search "triton_fp8_bmm mla warmup"
• gh issue list --repo vllm-project/vllm --state open --search "AITER MLA FP8 BMM warmup"

The only close related open PR I found is #52063, which is also mine. It is related but not a duplicate: #52063 changes the runtime K-side MLA projection path, while this PR changes startup precompile bounds for the AITER FP8 BMM path that remains. If #52063 lands first, this PR may need a small rebase in the same process_weights_after_loading area.

Test Plan

Local checks:

• pre-commit run --files vllm/model_executor/layers/attention/mla_attention.py tests/kernels/attention/test_rocm_aiter_mla_fp8_bmm_precompile.py
• ruff check vllm/model_executor/layers/attention/mla_attention.py tests/kernels/attention/test_rocm_aiter_mla_fp8_bmm_precompile.py
• python -m py_compile vllm/model_executor/layers/attention/mla_attention.py tests/kernels/attention/test_rocm_aiter_mla_fp8_bmm_precompile.py
• git diff --check
• pytest -q tests/kernels/attention/test_rocm_aiter_mla_fp8_bmm_precompile.py

RunPod MI300X smoke tests with deepseek-ai/DeepSeek-V2-Lite, ROCm AITER MLA, max_model_len=1024, input_len=128, output_len=1:

• batch 1 latency smoke
• batch 7 latency smoke, to verify an odd real decode size under the graph limit remains covered

Test Result

Local checks:

• pre-commit run --files ...: passed
• ruff check ...: passed
• python -m py_compile ...: passed
• git diff --check: passed
• PYTHONPATH=. uv run --no-sync --python 3.12 --with-requirements requirements/common.txt --with pytest --with tblib pytest -q tests/kernels/attention/test_rocm_aiter_mla_fp8_bmm_precompile.py: passed (9 passed, 14 warnings)

RunPod MI300X smoke results:

• batch 1: FP8 BMM precompile changed to 0/512; model load 58.38s; generation completed; avg latency 13.84 ms
• batch 7: FP8 BMM precompile changed to 0/512; model load 57.30s; generation completed; avg latency 63.4 ms

No JIT-monitor warnings appeared after inference monitor activation in either smoke test.

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